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Record W4416839901 · doi:10.70385/001c.151647

Canadian Perspectives: Writing a Future Care Cost (FCC) Report for Children Post TBI in Canada

2025· article· en· W4416839901 on OpenAlexaboutno aff
Galit Liffshiz, Ellen Drevnig

Bibliographic record

VenueJournal of Life Care Planning · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionTraumatic brain injuryPoison controlHuman factors and ergonomicsInjury preventionOccupational safety and healthSuicide prevention

Abstract

fetched live from OpenAlex

This article comprises a literature review of functional outcomes after Traumatic Brain Injury (TBI) in childhood and considerations for the preparation of the Future Cost of Care (FCC) report and valuing future needs and Canada. Children with TBI have dissimilar outcomes. Injury characteristics, environmental influences, and developmental factors affect outcomes. Injury severity plays a larger role for cognitive outcomes and the environment is important for behavioral and functional outcomes. Writing an FCC for a child with TBI should “restore” them to the position they would have been in if the accident had not occurred. This is challenging as the plan must adapt over their lifetime. Scenarios, and court decisions will be included.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.985

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.004
Science and technology studies0.0120.002
Scholarly communication0.0060.002
Open science0.0020.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0080.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.333
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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